Projects

Scraped 10-K Filings from the SEC website using BeautifulSoup and selected key sections for NLP Analysis to create a text based stock selection model based on an academic paper titled Lazy Prices.
Performed sentiment analysis on the 10-k's and evaluated the alpha factors by their Sharpe Ratio using the Alphalens library.

Implemented Using:
Python, BeautifulSoup, Alphalens, Pandas, Sklearn and Nltk Python libraries.

Stock Pairs Trading

Implemented Deep Q-Learning and defined the state, action and reward for a learning agent to be able to decide on trading actions for a pair of stocks.
Referred a research paper on forex trading pairs and used that as a baseline to code out the implementation strategy for a pair of stocks with the intention of maximizing returns.

Implemented Using:
Python, Keras, OpenAI, Numpy, Pandas, Reinforcement Learning

Fifa 19 Analysis

Various soccer related analyses using Naive Bayes, Logistic Regression, SVM and Clustering algorithms.
Developed an analysis tool to obtain like-for-like replacements for players based on various attributes and skill sets using clustering algorithms.

Implemented Using:
Python, Pandas, Numpy, Seaborn, Scikit-learn

Twitter Sentiment Anaysis

This app provides tweet sentiments for a user specified tweet topic and number of tweets the user would want to consider.

Implemented Using:
Python, Flask, Numpy, Tweepy, Google Cloud Platform